CN111539478A - Intelligent diagnosis method, system and diagnosis equipment for elevator faults - Google Patents
Intelligent diagnosis method, system and diagnosis equipment for elevator faults Download PDFInfo
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Abstract
The invention provides an intelligent elevator fault diagnosis method, which can determine target sensing information corresponding to each sensor at the moment of fault from cached sensing information when an elevator breaks down, then analyze the target sensing information of each sensor based on an information record table determined from a working condition database, determine the target fault occurrence rate of the part of the elevator detected by the sensor corresponding to the target sensing information, further determine the identification information of the sensor corresponding to the target fault occurrence rate when the target fault occurrence rate exceeds a set value, and finally determine the diagnostic code information corresponding to the identification information according to a preset mapping relation and output the diagnostic code information. Therefore, the fault position of the elevator can be output by using the diagnostic code information, accurate fault information is provided for a user and even a manufacturer, an engineer can quickly and accurately overhaul the fault information, the maintenance efficiency of the elevator is improved, and the maintenance time is saved.
Description
Technical Field
The invention relates to the technical field of fault diagnosis, in particular to an intelligent diagnosis method, system and diagnosis equipment for elevator faults.
Background
The elevator belongs to general large-scale mechanical equipment, and mechanical faults are inevitable in daily use. A common method of dealing with mechanical failure of an elevator is as follows: when the elevator breaks down, a user of the elevator feeds the fault condition back to a manufacturer, and the manufacturer arranges an engineer to carry out troubleshooting and maintenance on the failed elevator after receiving the feedback. However, because the elevator has more structures and spare and accessory parts, engineers spend more time in troubleshooting, and the maintenance efficiency is greatly influenced.
Disclosure of Invention
In order to solve the problems, the invention provides an intelligent diagnosis method, a system and a diagnosis device for elevator faults.
In a first aspect of an embodiment of the present invention, an intelligent elevator fault diagnosis method is provided, which is applied to a diagnosis device, the diagnosis device is disposed in a control box of an elevator, the elevator includes a guide rail, an elevator platform, a transmission device, and a hydraulic device, the elevator platform is connected to the guide rail, the transmission device, and the hydraulic device, the guide rail, the transmission device, and the hydraulic device are mutually matched to move the elevator platform, the guide rail, the elevator platform, the transmission device, and the hydraulic device are all provided with sensors, and the diagnosis device is in communication with each sensor, and the method includes:
acquiring and caching sensing information acquired by each sensor, wherein the sensing information comprises first position information used for representing the inclination of the lifting platform, second position information used for representing the position of the guide rail, acceleration information used for representing the running state of the transmission equipment and hydraulic information used for representing the working state of the hydraulic equipment;
detecting whether the elevator breaks down or not, and recording the moment of the failure when the elevator breaks down;
determining target sensing information corresponding to each sensor at the fault moment from the cached sensing information;
determining an information recording table of each target sensing information from a preset working condition database;
for each target sensing information, performing feature extraction on an information recording table corresponding to the target sensing information to obtain feature information of the information recording table under a plurality of time nodes; clustering the characteristic information corresponding to the information record table to obtain a plurality of clusters; determining a probability of occurrence of a fault for each cluster; extracting the features of the target sensing information to obtain a first feature vector; determining a target cluster where the target sensing information is located according to the similarity of the first feature vector and each cluster, and determining the target fault occurrence rate of the part of the elevator, detected by the sensor corresponding to the target sensing information, of the elevator according to the target cluster;
judging whether the target fault occurrence rate exceeds a set value, if so, determining the identification information of the sensor corresponding to the target fault occurrence rate, determining the diagnostic code information corresponding to the identification information according to a preset mapping relation, and outputting the diagnostic code information; wherein the diagnostic code information is used to characterize a location of the elevator failure.
In an alternative embodiment, the performing feature extraction on the information record table corresponding to the target sensing information to obtain feature information of the information record table under multiple time nodes includes:
acquiring the structural description information of the information record table;
splitting the information record table according to the structural description information of the information record table to obtain a plurality of groups of lists and text information included in each group of lists;
acquiring a list processing template, wherein the list processing template carries a time node numbering mode of each group of lists; the list processing template comprises a plurality of list processing intervals, and each list processing interval has the number sequence of the time nodes of the list;
acquiring the time node numbering mode of each group of lists from the list processing template, and determining a target numbering mode for carrying out time node numbering on the multiple groups of lists from the acquired time node numbering modes;
numbering each group of lists according to the text information corresponding to each group of lists and the target numbering mode to obtain a numbering result; sorting each group of lists according to the numbering result; sequentially inputting the text information in each group of ordered lists into each list processing interval of the list processing template;
and performing feature extraction on the text information in each list processing interval according to a feature extraction rule in each list processing interval to obtain feature information of the text information in each list processing interval under a time node corresponding to the number corresponding to each list processing interval.
In an alternative embodiment, the determining the probability of failure occurrence for each cluster comprises:
determining a characteristic value of the target object from each cluster; the target object is feature information of finishing clustering, the feature information is multi-dimensional feature information, the target object is an object of which the similarity value with any object except the target object in each cluster is smaller than a preset reference value, and the feature value is obtained through a second feature vector of the target object;
determining a time track curve corresponding to the target object according to the second characteristic vector of the target object, and mapping the characteristic value to the working condition track curve of the elevator determined according to each cluster according to the time track curve;
dividing the working condition track curve corresponding to each cluster according to the number of objects in each cluster to obtain a plurality of sections; and determining the fault occurrence probability of each cluster according to the positions of the target sections where the characteristic values are located in the plurality of sections.
In an alternative embodiment, the performing feature extraction on the target sensing information to obtain a first feature vector includes:
dividing numerical information contained in the target sensing information into different numerical information sets, wherein the set of the different numerical information sets contains all numerical information in the target sensing information, and at least two numerical information sets have an intersection;
respectively determining a first information characteristic weight, a second information characteristic weight and a third information characteristic weight corresponding to different numerical value information sets based on the probability of occurrence of target numerical value information in the different numerical value information sets, the proportion of the target numerical value information in the different numerical value information sets and the weight of the target numerical value information in the different numerical value information sets, wherein the first information characteristic weight, the second information characteristic weight and the third information characteristic weight are dimension values of information characteristics of the target sensing information respectively corresponding to the different numerical value information sets; wherein the target numerical value information is parameter information for representing the running state of the elevator;
determining a feature extraction list of the target sensing information according to a first information feature weight, a second information feature weight and a third information feature weight corresponding to the different numerical information sets, wherein the feature extraction list reflects feature vector extraction logic and sequence of the whole target sensing information;
and according to the feature extraction list, performing feature extraction on the target sensing information to obtain the first feature vector.
In an alternative embodiment, the outputting the diagnostic code information includes:
displaying the diagnostic code information in a display unit of the diagnostic apparatus; or
Sending the diagnostic code information to a user terminal in communication with the diagnostic equipment and enabling the user terminal to perform voice broadcast on the diagnostic code information; or
And sending the diagnosis code information to a manufacturer terminal which is communicated with the diagnosis equipment.
In an alternative embodiment, the method further comprises:
and performing associated storage on the diagnosis code information, the fault time and each target sensing information.
In an alternative embodiment, the acquiring the sensing information collected by each sensor includes:
acquiring a target signal sent by each sensor; the target signal is a radio frequency signal corresponding to sensing information acquired by each sensor;
identifying signal sequences included in each group of target signals to form signal sequences with different lengths corresponding to each group of target signals, wherein the distortion rates of the signal sequences with different lengths are different; wherein the signal sequence comprises a plurality of signal strength values between two consecutive radio frequency instants;
determining a signal category identification result of each group of target signals;
selecting a target signal corresponding to a target signal type as a signal to be repaired from the signal type identification result, wherein the target signal type is a signal type with a signal distortion rate exceeding a preset ratio;
determining a signal sequence with a length greater than a set length in the signal to be repaired as a signal sequence to be repaired;
screening a target intensity value meeting a preset signal intensity detection condition from the signal sequence to be repaired, adjusting the target intensity value, and determining a repaired signal corresponding to each sensor according to the adjusted target intensity value; and converting the repaired signals corresponding to each sensor into analog signals, and determining the sensing information acquired by each sensor according to the analog signals.
In a second aspect of the embodiments of the present invention, an intelligent elevator fault diagnosis system is provided, including a diagnosis device, where the diagnosis device is disposed in a control box of an elevator, the elevator includes a guide rail, an elevator platform, a transmission device, and a hydraulic device, the elevator platform is connected with the guide rail, the transmission device, and the hydraulic device, and the guide rail, the transmission device, and the hydraulic device are mutually matched to move the elevator platform; the intelligent elevator fault diagnosis system also comprises sensors which are respectively arranged on the guide rail, the lifting platform, the transmission equipment and the hydraulic equipment, and the diagnosis equipment is communicated with each sensor;
the sensor is used for sending the acquired sensing information to the diagnostic equipment; the sensing information comprises first position information used for representing the inclination of the lifting platform, second position information used for representing the position of the guide rail, acceleration information used for representing the running state of the transmission device and hydraulic information used for representing the working state of the hydraulic device;
the diagnostic equipment is used for acquiring and caching the sensing information acquired by each sensor; detecting whether the elevator breaks down or not, and recording the moment of the failure when the elevator breaks down; determining target sensing information corresponding to each sensor at the fault moment from the cached sensing information; determining an information recording table of each target sensing information from a preset working condition database; for each target sensing information, performing feature extraction on an information recording table corresponding to the target sensing information to obtain feature information of the information recording table under a plurality of time nodes; clustering the characteristic information corresponding to the information record table to obtain a plurality of clusters; determining a probability of occurrence of a fault for each cluster; extracting the features of the target sensing information to obtain a first feature vector; determining a target cluster where the target sensing information is located according to the similarity of the first feature vector and each cluster, and determining the target fault occurrence rate of the part of the elevator, detected by the sensor corresponding to the target sensing information, of the elevator according to the target cluster; judging whether the target fault occurrence rate exceeds a set value, if so, determining the identification information of the sensor corresponding to the target fault occurrence rate, determining the diagnostic code information corresponding to the identification information according to a preset mapping relation, and outputting the diagnostic code information; wherein the diagnostic code information is used to characterize a location of the elevator failure.
In a third aspect of the embodiments of the present invention, there is provided a diagnostic apparatus including:
the acquisition module is used for acquiring and caching sensing information acquired by each sensor, wherein the sensing information comprises first position information used for representing the inclination of the lifting platform, second position information used for representing the position of the guide rail, acceleration information used for representing the running state of the transmission equipment and hydraulic information used for representing the working state of the hydraulic equipment;
the determining module is used for detecting whether the elevator breaks down or not and recording the moment of the fault when the elevator breaks down; determining target sensing information corresponding to each sensor at the fault moment from the cached sensing information; determining an information recording table of each target sensing information from a preset working condition database;
the characteristic extraction module is used for extracting the characteristics of the information record table corresponding to the target sensing information aiming at each target sensing information to obtain the characteristic information of the information record table under a plurality of time nodes; clustering the characteristic information corresponding to the information record table to obtain a plurality of clusters; determining a probability of occurrence of a fault for each cluster; extracting the features of the target sensing information to obtain a first feature vector; determining a target cluster where the target sensing information is located according to the similarity of the first feature vector and each cluster, and determining the target fault occurrence rate of the part of the elevator, detected by the sensor corresponding to the target sensing information, of the elevator according to the target cluster;
the judging module is used for judging whether the target fault occurrence rate exceeds a set value or not, if so, determining the identification information of the sensor corresponding to the target fault occurrence rate, determining the diagnostic code information corresponding to the identification information according to a preset mapping relation and outputting the diagnostic code information; wherein the diagnostic code information is used to characterize a location of the elevator failure.
In a fourth aspect of the embodiments of the present invention, there is provided a diagnostic apparatus including: a processor and a memory and bus connected to the processor; the processor and the memory are communicated with each other through the bus; the processor is used for calling the computer program in the memory to execute the intelligent elevator fault diagnosis method.
The intelligent diagnosis method, system and diagnosis equipment for elevator faults provided by the embodiment of the invention can determine the target sensing information corresponding to each sensor at the moment of the fault from the cached sensing information when the elevator has the fault, then analyze the target sensing information of each sensor based on the information record table determined from the working condition database, determine the target fault occurrence rate of the part of the elevator detected by the sensor corresponding to the target sensing information, further determine the identification information of the sensor corresponding to the target fault occurrence rate when the target fault occurrence rate exceeds the set value, finally determine the diagnosis code information corresponding to the identification information according to the preset mapping relation and output the diagnosis code information, thus, the part of the elevator with the fault can be output by using the diagnosis code information, and accurate fault information is provided for a user or even a manufacturer, and then make the engineer can overhaul according to fault information fast, accurately, improve the maintenance efficiency of lift, save maintenance duration.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic structural diagram of an elevator fault intelligent diagnosis system according to an embodiment of the present invention.
Fig. 2 is a flowchart of an elevator fault intelligent diagnosis method according to an embodiment of the present invention.
Fig. 3 is a functional block diagram of a diagnostic apparatus according to an embodiment of the present invention.
Icon:
100-elevator fault intelligent diagnosis system;
1-a diagnostic device; 11-an acquisition module; 12-a determination module; 13-a feature extraction module; 14-a judgment module;
2-a sensor;
3-a lifter; 31-a guide rail; 32-a lifting platform; 33-a transmission device; 34-hydraulic equipment.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.
Referring to fig. 1, a schematic structural diagram of an elevator fault intelligent diagnosis system 100 according to an embodiment of the present invention is shown, where the elevator fault intelligent diagnosis system 100 includes a diagnosis device 1 and sensors 2 disposed at various positions of an elevator 3.
In the present embodiment, the elevator 3 may include a guide rail 31, an elevating platform 32, a transmission device 33, a hydraulic device 34, and the like. Accordingly, sensors 2 are provided on the guide rail 31, the lifting platform 32, the transmission device 33 and the hydraulic device 34, each sensor 2 communicating with the diagnostic device 1.
In the present embodiment, the diagnostic device 1 may be an electronic device having data information processing, and the diagnostic device 1 may be provided in a control box of the elevator 3 or at another position of the elevator as long as instant communication with each sensor 2 can be ensured.
In the system, the sensor 2 may be configured to send the acquired sensing information to the diagnostic device; the sensing information comprises first position information for characterizing the inclination of the lifting platform, second position information for characterizing the position of the guide rail, acceleration information for characterizing the operating state of the transmission device, and hydraulic information for characterizing the operating state of the hydraulic device.
Further, the diagnostic apparatus 1 may be configured to acquire and cache sensing information acquired by each sensor; detecting whether the elevator breaks down or not, and recording the moment of the failure when the elevator breaks down; determining target sensing information corresponding to each sensor at the fault moment from the cached sensing information; and determining an information recording table of each target sensing information from a preset working condition database.
Furthermore, the diagnostic apparatus 1 is further configured to, for each target sensing information, perform feature extraction on an information record table corresponding to the target sensing information to obtain feature information of the information record table under multiple time nodes; clustering the characteristic information corresponding to the information record table to obtain a plurality of clusters; determining a probability of occurrence of a fault for each cluster; extracting the features of the target sensing information to obtain a first feature vector; and determining a target cluster where the target sensing information is located according to the similarity of the first feature vector and each cluster, and determining the target fault occurrence rate of the part of the elevator, which is detected by the sensor corresponding to the target sensing information, according to the target cluster.
It can be understood that, after the diagnostic device 1 determines each target failure occurrence rate, it is further configured to determine whether the target failure occurrence rate exceeds a set value, if so, determine identification information of a sensor corresponding to the target failure occurrence rate, determine diagnostic code information corresponding to the identification information according to a preset mapping relationship, and output the diagnostic code information.
In this embodiment, the diagnostic code information is used to characterize the location of the elevator failure.
It can be understood that, based on the above system, when the elevator fails, the target sensing information corresponding to each sensor at the time of the failure can be determined from the cached sensing information, then the target sensing information of each sensor is analyzed based on the information record table determined from the working condition database, the target failure occurrence rate of the part of the elevator detected by the sensor corresponding to the target sensing information is determined, further the identification information of the sensor corresponding to the target failure occurrence rate is determined when the target failure occurrence rate exceeds the set value, finally the diagnostic code information corresponding to the identification information is determined according to the preset mapping relationship and the diagnostic code information is output, so that the part of the elevator where the failure occurs can be output by using the diagnostic code information, accurate failure information is provided for users and even manufacturers, and further engineers can rapidly and accurately determine the target sensing information according to the failure information, The maintenance is accurately carried out, the maintenance efficiency of the elevator is improved, and the maintenance time is saved.
Referring to fig. 2, a flowchart of an elevator fault intelligent diagnosis method according to an embodiment of the present invention is provided, where the method is applied to the diagnosis apparatus 1 in fig. 1, and further, the method may include the following steps.
And step S21, acquiring and caching the sensing information collected by each sensor.
In step S21, the sensing information includes first position information for representing an inclination of the lifting platform, second position information for representing a position of the guide rail, acceleration information for representing an operating state of the transmission device, and hydraulic information for representing an operating state of the hydraulic device. In this embodiment, the diagnostic device may dynamically cache the sensing information, where dynamic caching may be understood as being capable of modifying the sensing information cached in the diagnostic device.
And step S22, detecting whether the elevator has a fault or not, and recording the fault time when the elevator has the fault.
And step S23, determining target sensing information corresponding to each sensor at the fault time from the cached sensing information.
And step S24, determining an information recording table of each target sensing information from a preset working condition database.
In step S24, in the present embodiment, the operating condition database is used to store the operating information of the elevator under different operating conditions. It is understood that the information record table corresponding to each target sensing information may be all history information of the elevator since the elevator was operated from the factory.
For example, the information recording table corresponding to the hydraulic controller may store all historical hydraulic pressure information of the elevator since the factory operation. It is understood that the historical hydraulic pressure information includes hydraulic pressure information when the elevator is operating normally and has a fault.
Step S25, for each target sensing information, extracting the characteristics of the information record table corresponding to the target sensing information to obtain the characteristic information of the information record table under a plurality of time nodes; clustering the characteristic information corresponding to the information record table to obtain a plurality of clusters; determining a probability of occurrence of a fault for each cluster; extracting the features of the target sensing information to obtain a first feature vector; and determining a target cluster where the target sensing information is located according to the similarity of the first feature vector and each cluster, and determining the target fault occurrence rate of the part of the elevator, which is detected by the sensor corresponding to the target sensing information, according to the target cluster.
In step S25, the feature information may be multi-dimensional information, and accordingly, the feature information may be clustered by a K-means clustering method.
Step S26, judging whether the target failure occurrence rate exceeds a set value, if so, determining the identification information of the sensor corresponding to the target failure occurrence rate, determining the diagnostic code information corresponding to the identification information according to a preset mapping relation, and outputting the diagnostic code information; wherein the diagnostic code information is used to characterize a location of the elevator failure.
In step S26, the setting values of different sensors are different, and the setting values may be determined according to the model and the breakage factor of the sensor.
In step S26, the identification information is used to distinguish the sensors. The preset mapping is used to match each sensor to a respective part of the elevator. Further, the diagnostic code information may be used to characterize the location of the elevator failure.
For example, in the diagnostic code information, english letters can be used to distinguish each part of the elevator. Wherein the lifting platform can be represented by a, the guide rail by B, the transmission device by C and the hydraulic device by D.
Also for example, the orientation and structure points of each part of the elevator can be represented by arabic numerals. Further, C1 may be used to represent the drive section of the drive device and A3 may be used to represent the apex point of the lift platform.
In step S26, the diagnosis device may output the diagnosis code information, for example, the diagnosis code information may be displayed, so as to provide accurate fault information for the user or even the manufacturer, so that the engineer can quickly and accurately perform maintenance according to the fault information, thereby improving the maintenance efficiency of the elevator and saving the maintenance time.
It can be understood that, through steps S21-S26, when the elevator fails, the target sensing information corresponding to each sensor at the time of the failure is determined from the cached sensing information, then the target sensing information of each sensor is analyzed based on the information record table determined from the working condition database, the target failure occurrence rate of the part of the elevator detected by the sensor corresponding to the target sensing information is determined, further the identification information of the sensor corresponding to the target failure occurrence rate is determined when the target failure occurrence rate exceeds the set value, finally the diagnostic code information corresponding to the identification information is determined according to the preset mapping relation and the diagnostic code information is output, so that the part of the elevator where the failure occurs can be output by the diagnostic code information, accurate failure information is provided for users and manufacturers, and further engineers can rapidly determine the target sensing information corresponding to each sensor according to the failure information, The maintenance is accurately carried out, the maintenance efficiency of the elevator is improved, and the maintenance time is saved.
In a specific implementation, in order to ensure the comprehensiveness of the feature extraction, in step S25, the feature extraction is performed on the information record table corresponding to the target sensing information to obtain the feature information of the information record table at a plurality of time nodes, which may specifically include the following.
Step S2511, obtains the structural description information of the information record table.
Step S2512, splitting the information record table according to the structural description information of the information record table to obtain multiple lists and text information included in each list.
Step S2513, a list processing template is obtained, wherein the list processing template carries the time node numbering mode of each group of lists; the list handling template includes a plurality of list handling intervals, and each list handling interval has a numbering order of time nodes of the list.
Step S2514, acquiring the time node numbering modes of each group of lists from the list processing template, and determining the target numbering modes for time node numbering of the multiple groups of lists from the acquired time node numbering modes.
Step S2515, numbering each group of lists according to the text information corresponding to each group of lists and the target numbering mode to obtain a numbering result; sorting each group of lists according to the numbering result; and sequentially inputting the text information in each group of ordered lists into each list processing interval of the list processing template.
Step S2516, according to the feature extraction rule in each list processing section, feature extraction is performed on the text information in each list processing section, so as to obtain feature information of the text information in each list processing section at the time node corresponding to the number corresponding to each list processing section.
It is understood that through steps S2511 to S2516, the information record table can be split based on the structural description information of the information record table, and then feature extraction can be performed based on the multiple sets of lists obtained by splitting, so that the comprehensiveness of feature extraction can be ensured.
In practical applications, the failure occurrence probability corresponding to each cluster is different, and in order to accurately determine the failure occurrence probability corresponding to each cluster, in step S25, the determining the failure occurrence probability of each cluster may specifically include the following.
In step S2521, a feature value of the target object is determined from each cluster.
In step S2521, the target object is feature information of completed clusters, the feature information is multidimensional feature information, the target object is an object in each cluster whose similarity to any object other than the target object is smaller than a preset reference value, and the feature value is obtained by a second feature vector of the target object.
Step S2522, a time trajectory curve corresponding to the target object is determined according to the second eigenvector of the target object, and the eigenvalue is mapped into the working condition trajectory curve of the elevator determined according to each cluster according to the time trajectory curve.
Step S2523, according to the number of objects in each cluster, dividing a working condition track curve corresponding to each cluster to obtain a plurality of sections; and determining the fault occurrence probability of each cluster according to the positions of the target sections where the characteristic values are located in the plurality of sections.
In this embodiment, based on the above, the failure occurrence probability corresponding to each cluster can be accurately determined.
In a specific implementation, in order to ensure the accuracy of the first feature vector, in step S25, the feature extraction on the target sensing information to obtain the first feature vector may specifically include the following.
In step S2531, the numerical information included in the target sensing information is divided into different numerical information sets.
In step S2531, the collection of different sets of numerical information includes all the numerical information in the target sensing information, and at least two sets of numerical information have an intersection.
Step S2532, based on the probability of occurrence of the target numerical information in the different numerical information sets, the percentage of the target numerical information in the different numerical information sets, and the weight of the target numerical information in the different numerical information sets, respectively determine a first information feature weight, a second information feature weight, and a third information feature weight corresponding to the different numerical information sets.
In step S2532, the first information characteristic weight, the second information characteristic weight, and the third information characteristic weight are dimension values of information characteristics of the target sensing information respectively corresponding to the different numerical information sets; the target numerical information is parameter information for characterizing the running state of the elevator.
Step S2533, determining a feature extraction list of the target sensing information according to the first information feature weight, the second information feature weight, and the third information feature weight corresponding to the different numerical information sets.
In step S2533, the feature extraction list reflects the feature vector extraction logic and the order of the entire target sensor information.
And step S2534, extracting the features of the target sensing information according to the feature extraction list to obtain the first feature vector.
It is understood that through steps 2531 to 2534, the numerical information in the target sensing information can be divided to obtain different numerical information sets. And then analyzing based on target numerical information in different numerical information sets to further determine a feature extraction list of the target sensing information, so that feature extraction can be performed on the target sensing information based on the feature extraction list, and the first feature vector can be accurately obtained.
In particular, the diagnostic device 1 may output the diagnostic code information in the following manner.
(1) And displaying the diagnosis code information in a display unit of the diagnosis equipment.
(2) And sending the diagnosis code information to a user terminal communicated with the diagnosis equipment and enabling the user terminal to carry out voice broadcast on the diagnosis code information.
(3) And sending the diagnosis code information to a manufacturer terminal which is communicated with the diagnosis equipment.
It is understood that the manner in which the diagnostic device 1 outputs the diagnostic code information may be adjusted according to actual circumstances, and thus is not limited herein.
Optionally, on the basis of the steps S21-S26, the method may further include the following: and performing associated storage on the diagnosis code information, the fault time and each target sensing information. Therefore, the diagnostic basis can be provided for subsequent fault analysis, so that a quick decision basis can be provided for subsequent similar faults.
In practical applications, in order to ensure the accuracy of obtaining the sensing information of each sensor and avoid distortion of the sensing information during transmission, in step S21, the obtaining of the sensing information collected by each sensor may specifically include the following.
Step S211, a target signal transmitted by each sensor is acquired.
In step S211, the target signal is a radio frequency signal corresponding to the sensing information collected by each sensor.
Step S212, identify the signal sequences included in each group of target signals, and form signal sequences with different lengths corresponding to each group of target signals, where the distortion rates of the signal sequences with different lengths are different.
In step S212, the signal sequence includes a plurality of signal strength values between two consecutive radio frequency time instants.
In step S213, a signal type identification result of each group of target signals is determined.
Step S214, selecting a target signal corresponding to the target signal category as a signal to be repaired from the signal category identification result.
In step S214, the target signal class is a signal class with a signal distortion ratio exceeding a preset ratio.
Step S215, determining a signal sequence with a length greater than a set length in the signal to be repaired as a signal sequence to be repaired.
Step S216, screening a target intensity value meeting preset signal intensity detection conditions from the signal sequence to be repaired, adjusting the target intensity value, and determining a repaired signal corresponding to each sensor according to the adjusted target intensity value; and converting the repaired signals corresponding to each sensor into analog signals, and determining the sensing information acquired by each sensor according to the analog signals.
It can be understood that, through steps S211 to S216, the distortion of the target signal in the radio frequency transmission process can be repaired before the target signal is subjected to analog-to-digital conversion, and then the analog signal is determined after the repaired signal corresponding to the target signal is obtained, and then the sensing information is determined. Therefore, the accuracy of acquiring the sensing information of each sensor can be ensured, and the extra increased calculation amount caused by repairing the analog signals can be avoided.
On the basis of the above, please refer to fig. 3, which is a block diagram of a diagnostic apparatus 1 according to an embodiment of the present invention, wherein the diagnostic apparatus 1 may include the following modules.
The acquisition module 11 is configured to acquire and cache sensing information acquired by each sensor, where the sensing information includes first position information used for representing an inclination of the lifting platform, second position information used for representing a position of the guide rail, acceleration information used for representing an operating state of the transmission device, and hydraulic information used for representing a working state of the hydraulic device.
The determining module 12 is used for detecting whether the elevator breaks down or not and recording the moment of the failure when the elevator breaks down; determining target sensing information corresponding to each sensor at the fault moment from the cached sensing information; and determining an information recording table of each target sensing information from a preset working condition database.
The feature extraction module 13 is configured to perform feature extraction on an information record table corresponding to each target sensing information to obtain feature information of the information record table under multiple time nodes; clustering the characteristic information corresponding to the information record table to obtain a plurality of clusters; determining a probability of occurrence of a fault for each cluster; extracting the features of the target sensing information to obtain a first feature vector; and determining a target cluster where the target sensing information is located according to the similarity of the first feature vector and each cluster, and determining the target fault occurrence rate of the part of the elevator, which is detected by the sensor corresponding to the target sensing information, according to the target cluster.
A judging module 14, configured to judge whether the target failure occurrence rate exceeds a set value, if so, determine identification information of a sensor corresponding to the target failure occurrence rate, determine diagnostic code information corresponding to the identification information according to a preset mapping relationship, and output the diagnostic code information; wherein the diagnostic code information is used to characterize a location of the elevator failure.
The embodiment of the invention also provides a readable storage medium, wherein a program is stored on the readable storage medium, and the program realizes the intelligent diagnosis method for the elevator fault when being executed by a processor.
The embodiment of the invention provides a processor, which is used for running a program, wherein the intelligent diagnosis method for the elevator fault is executed when the program runs.
In this embodiment, the diagnostic apparatus 1 includes at least one processor, and at least one memory and a bus connected to the processor. The processor and the memory complete mutual communication through the bus. The processor is used for calling the program instructions in the memory to execute the intelligent elevator fault diagnosis method.
To sum up, the elevator fault intelligent diagnosis method, system and diagnosis device provided by the embodiments of the present invention can determine target sensing information corresponding to each sensor at a fault time from the cached sensing information when the elevator has a fault, then analyze the target sensing information of each sensor based on the information record table determined from the working condition database, determine a target fault occurrence rate of a part of the elevator detected by the sensor corresponding to the target sensing information, further determine identification information of the sensor corresponding to the target fault occurrence rate when the target fault occurrence rate exceeds a set value, finally determine diagnosis code information corresponding to the identification information according to a preset mapping relationship and output the diagnosis code information, so that the part of the elevator having a fault can be output by using the diagnosis code information, thereby providing accurate fault information for a user or even a manufacturer, and then make the engineer can overhaul according to fault information fast, accurately, improve the maintenance efficiency of lift, save maintenance duration.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, cloud diagnostic devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing cloud diagnostic apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing cloud diagnostic apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a cloud diagnostic device includes one or more processors (CPUs), memory, and a bus. The cloud diagnostic device may also include an input/output interface, a network interface, and the like.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage cloud diagnostic devices, or any other non-transmission medium that can be used to store information that can be matched by a computing cloud diagnostic device. As defined herein, computer readable media does not include transitory computer readable media such as modulated data signals and carrier waves.
It is also to be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or cloud diagnostic apparatus that comprises a list of elements does not include only those elements but also other elements not expressly listed or inherent to such process, method, article, or cloud diagnostic apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of additional like elements in the process, method, article of manufacture, or cloud diagnostic device comprising the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. An intelligent diagnosis method for elevator faults is applied to a diagnosis device, the diagnosis device is arranged in a control box of an elevator, the elevator comprises a guide rail, an elevating platform, a transmission device and a hydraulic device, the elevating platform is connected with the guide rail, the transmission device and the hydraulic device, the guide rail, the transmission device and the hydraulic device are mutually matched to enable the elevating platform to ascend and descend, the guide rail, the elevating platform, the transmission device and the hydraulic device are all provided with sensors, and the diagnosis device is in communication connection with each sensor, and the method comprises the following steps:
acquiring and caching sensing information acquired by each sensor, wherein the sensing information comprises first position information used for representing the inclination of the lifting platform, second position information used for representing the position of the guide rail, acceleration information used for representing the running state of the transmission equipment and hydraulic information used for representing the working state of the hydraulic equipment;
detecting whether the elevator breaks down or not, and recording the moment of the failure when the elevator breaks down;
determining target sensing information corresponding to each sensor at the fault moment from the cached sensing information;
determining an information recording table of each target sensing information from a preset working condition database;
for each target sensing information, performing feature extraction on an information recording table corresponding to the target sensing information to obtain feature information of the information recording table under a plurality of time nodes; clustering the characteristic information corresponding to the information record table to obtain a plurality of clusters; determining a probability of occurrence of a fault for each cluster; extracting the features of the target sensing information to obtain a first feature vector; determining a target cluster where the target sensing information is located according to the similarity of the first feature vector and each cluster, and determining the target fault occurrence rate of the part of the elevator, detected by the sensor corresponding to the target sensing information, of the elevator according to the target cluster;
judging whether the target fault occurrence rate exceeds a set value, if so, determining the identification information of the sensor corresponding to the target fault occurrence rate, determining the diagnostic code information corresponding to the identification information according to a preset mapping relation, and outputting the diagnostic code information; wherein the diagnostic code information is used to characterize a location of the elevator failure.
2. The method according to claim 1, wherein the extracting the features of the information record table corresponding to the target sensing information to obtain the feature information of the information record table under a plurality of time nodes includes:
acquiring the structural description information of the information record table;
splitting the information record table according to the structural description information of the information record table to obtain a plurality of groups of lists and text information included in each group of lists;
acquiring a list processing template, wherein the list processing template carries a time node numbering mode of each group of lists; the list processing template comprises a plurality of list processing intervals, and each list processing interval has the number sequence of the time nodes of the list;
acquiring the time node numbering mode of each group of lists from the list processing template, and determining a target numbering mode for carrying out time node numbering on the multiple groups of lists from the acquired time node numbering modes;
numbering each group of lists according to the text information corresponding to each group of lists and the target numbering mode to obtain a numbering result; sorting each group of lists according to the numbering result; sequentially inputting the text information in each group of ordered lists into each list processing interval of the list processing template;
and performing feature extraction on the text information in each list processing interval according to a feature extraction rule in each list processing interval to obtain feature information of the text information in each list processing interval under a time node corresponding to the number corresponding to each list processing interval.
3. The method of claim 1 or 2, wherein the determining the probability of failure occurrence for each cluster comprises:
determining a characteristic value of the target object from each cluster; the target object is feature information of finishing clustering, the feature information is multi-dimensional feature information, the target object is an object of which the similarity value with any object except the target object in each cluster is smaller than a preset reference value, and the feature value is obtained through a second feature vector of the target object;
determining a time track curve corresponding to the target object according to the second characteristic vector of the target object, and mapping the characteristic value to the working condition track curve of the elevator determined according to each cluster according to the time track curve;
dividing the working condition track curve corresponding to each cluster according to the number of objects in each cluster to obtain a plurality of sections; and determining the fault occurrence probability of each cluster according to the positions of the target sections where the characteristic values are located in the plurality of sections.
4. The method of claim 1, wherein the performing feature extraction on the target sensing information to obtain a first feature vector comprises:
dividing numerical information contained in the target sensing information into different numerical information sets, wherein the set of the different numerical information sets contains all numerical information in the target sensing information, and at least two numerical information sets have an intersection;
respectively determining a first information characteristic weight, a second information characteristic weight and a third information characteristic weight corresponding to different numerical value information sets based on the probability of occurrence of target numerical value information in the different numerical value information sets, the proportion of the target numerical value information in the different numerical value information sets and the weight of the target numerical value information in the different numerical value information sets, wherein the first information characteristic weight, the second information characteristic weight and the third information characteristic weight are dimension values of information characteristics of the target sensing information respectively corresponding to the different numerical value information sets; wherein the target numerical value information is parameter information for representing the running state of the elevator;
determining a feature extraction list of the target sensing information according to a first information feature weight, a second information feature weight and a third information feature weight corresponding to the different numerical information sets, wherein the feature extraction list reflects feature vector extraction logic and sequence of the whole target sensing information;
and according to the feature extraction list, performing feature extraction on the target sensing information to obtain the first feature vector.
5. The method of claim 1, wherein the outputting the diagnostic code information comprises:
displaying the diagnostic code information in a display unit of the diagnostic apparatus; or
Sending the diagnostic code information to a user terminal in communication with the diagnostic equipment and enabling the user terminal to perform voice broadcast on the diagnostic code information; or
And sending the diagnosis code information to a manufacturer terminal which is communicated with the diagnosis equipment.
6. The method of claim 1, further comprising:
and performing associated storage on the diagnosis code information, the fault time and each target sensing information.
7. The method of claim 1, wherein the acquiring the sensing information collected by each sensor comprises:
acquiring a target signal sent by each sensor; the target signal is a radio frequency signal corresponding to sensing information acquired by each sensor;
identifying signal sequences included in each group of target signals to form signal sequences with different lengths corresponding to each group of target signals, wherein the distortion rates of the signal sequences with different lengths are different; wherein the signal sequence comprises a plurality of signal strength values between two consecutive radio frequency instants;
determining a signal category identification result of each group of target signals;
selecting a target signal corresponding to a target signal type as a signal to be repaired from the signal type identification result, wherein the target signal type is a signal type with a signal distortion rate exceeding a preset ratio;
determining a signal sequence with a length greater than a set length in the signal to be repaired as a signal sequence to be repaired;
screening a target intensity value meeting a preset signal intensity detection condition from the signal sequence to be repaired, adjusting the target intensity value, and determining a repaired signal corresponding to each sensor according to the adjusted target intensity value; and converting the repaired signals corresponding to each sensor into analog signals, and determining the sensing information acquired by each sensor according to the analog signals.
8. An intelligent elevator fault diagnosis system is characterized by comprising diagnosis equipment, wherein the diagnosis equipment is arranged in a control box of an elevator, the elevator comprises a guide rail, an elevator platform, transmission equipment and hydraulic equipment, the elevator platform is connected with the guide rail, the transmission equipment and the hydraulic equipment, and the guide rail, the transmission equipment and the hydraulic equipment are mutually matched to enable the elevator platform to move; the intelligent elevator fault diagnosis system also comprises sensors which are respectively arranged on the guide rail, the lifting platform, the transmission equipment and the hydraulic equipment, and the diagnosis equipment is communicated with each sensor;
the sensor is used for sending the acquired sensing information to the diagnostic equipment; the sensing information comprises first position information used for representing the inclination of the lifting platform, second position information used for representing the position of the guide rail, acceleration information used for representing the running state of the transmission device and hydraulic information used for representing the working state of the hydraulic device;
the diagnostic equipment is used for acquiring and caching the sensing information acquired by each sensor; detecting whether the elevator breaks down or not, and recording the moment of the failure when the elevator breaks down; determining target sensing information corresponding to each sensor at the fault moment from the cached sensing information; determining an information recording table of each target sensing information from a preset working condition database; for each target sensing information, performing feature extraction on an information recording table corresponding to the target sensing information to obtain feature information of the information recording table under a plurality of time nodes; clustering the characteristic information corresponding to the information record table to obtain a plurality of clusters; determining a probability of occurrence of a fault for each cluster; extracting the features of the target sensing information to obtain a first feature vector; determining a target cluster where the target sensing information is located according to the similarity of the first feature vector and each cluster, and determining the target fault occurrence rate of the part of the elevator, detected by the sensor corresponding to the target sensing information, of the elevator according to the target cluster; judging whether the target fault occurrence rate exceeds a set value, if so, determining the identification information of the sensor corresponding to the target fault occurrence rate, determining the diagnostic code information corresponding to the identification information according to a preset mapping relation, and outputting the diagnostic code information; wherein the diagnostic code information is used to characterize a location of the elevator failure.
9. A diagnostic device, comprising:
the acquisition module is used for acquiring and caching sensing information acquired by each sensor, wherein the sensing information comprises first position information used for representing the inclination of the lifting platform, second position information used for representing the position of the guide rail, acceleration information used for representing the running state of the transmission equipment and hydraulic information used for representing the working state of the hydraulic equipment;
the determining module is used for detecting whether the elevator breaks down or not and recording the moment of the fault when the elevator breaks down; determining target sensing information corresponding to each sensor at the fault moment from the cached sensing information; determining an information recording table of each target sensing information from a preset working condition database;
the characteristic extraction module is used for extracting the characteristics of the information record table corresponding to the target sensing information aiming at each target sensing information to obtain the characteristic information of the information record table under a plurality of time nodes; clustering the characteristic information corresponding to the information record table to obtain a plurality of clusters; determining a probability of occurrence of a fault for each cluster; extracting the features of the target sensing information to obtain a first feature vector; determining a target cluster where the target sensing information is located according to the similarity of the first feature vector and each cluster, and determining the target fault occurrence rate of the part of the elevator, detected by the sensor corresponding to the target sensing information, of the elevator according to the target cluster;
the judging module is used for judging whether the target fault occurrence rate exceeds a set value or not, if so, determining the identification information of the sensor corresponding to the target fault occurrence rate, determining the diagnostic code information corresponding to the identification information according to a preset mapping relation and outputting the diagnostic code information; wherein the diagnostic code information is used to characterize a location of the elevator failure.
10. A diagnostic device, comprising: a processor and a memory and bus connected to the processor; the processor and the memory are communicated with each other through the bus; the processor is used for calling a computer program in the memory to execute the elevator fault intelligent diagnosis method of any one of the preceding claims 1-7.
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